Engineering Commons^™

Artificial Intelligence Event Video Collection (2025), Peaaii Umass Boston

Paul English Applied Artificial Intelligence (AI) Institute Publications

This submission contains a collection of recorded videos and promotional materials from artificial intelligence events organized by the Paul English Applied AI Institute (PEAAII) at the University of Massachusetts Boston in 2025. These events include AI Frontier Day, the Fall Symposium, and the AI Applications Hackathon.

The video collection highlights student research, academic collaboration, and applied learning experiences in artificial intelligence. These materials are intended to support education, increase accessibility to AI-related content, and showcase the work of students and faculty involved in PEAAII programs.

Tidal Flooding Contributes To Eutrophication: Constraining Nonpoint Source Inputs To An Urban Estuary Using A Data Driven Statistical Model, Alfonso Macías-Tapia, Margaret R. Mulholland, Corday R. Selden, Sophie Clayton, Peter W. Bernhardt, Thomas R. Allen

OES Faculty Publications

In coastal urban areas, tidal flooding brings water carrying nutrients and particles back from land to estuarine and coastal waters. A statistical model to predict nutrient loads during tidal flooding events can help estimate nutrient loading from previous and future flooding events and adapt nutrient reduction strategies. We measured concentrations of dissolved inorganic nitrogen and phosphorus in floodwater at seven sentinel sites during 15 tidal flooding events from January 2019 to September 2020. The study area was the Lafayette River watershed in Norfolk, VA, USA, which is prone to tidal flooding and is predicted to experience more frequent and intense …

Internally Stowed, Radially Deployed Radiator Panels For Passive Cubesat Thermal Control, Josh R. Cannon, Kyle N. Havey, Noah S. Housley, Rydge B. Mulford, Brian D. Iverson

Faculty Publications

CubeSats experience significant thermal loads due to solar irradiation and the dissipation from electrical components. The high heat dissipation per unit volume can lead to mission failure if not properly managed. Deployable radiators that are externally stowed and passively actuated in response to changes in CubeSat temperature have been explored as a viable solution. This work describes a radially deployed fin array that is stowed within the CubeSat body when the required heat dissipation is low and passively deploys when the required heat dissipation is high. Internal stowage improves thermal transport to the deployable fins and minimizes heat loss when …

Drop Dynamics During Condensation On Superhydrophobic Surfaces In Vapor Shear Flow, Shaur Humayun, R. Daniel Maynes, Julie Crockett, Brian D. Iverson

Faculty Publications

Accurate models for predicting drop dynamics, such as maximum drop departure sizes, are crucial for estimating heat transfer rates during condensation on superhydrophobic (SH) surfaces. Previous studies have focused on examining the heat transfer rates for SH surfaces under the influence of gravity or vapor flowing over the surface. This study investigates the impact of surface solid fraction and texture scale on drop mobility in a condensing environment with a humid air flow. Experiments recorded condensation with varying surface feature sizes from micro- to nano scale under different flow rates. Video analysis detected the drop-size distribution and maximum drop departure …

Origami Folding Pattern Development For Gossamer 18 Structures, Larry L. Howell, Ivy Running, Carolina Wright, Robert J. Lang, Spencer Magleby

Faculty Publications

Gossamer structures for satellites are large, thin deployable structures that are attractive for space applications because they can be stowed compactly for transportation and deployed to have large functional surface areas. Reflectarray antennas, in particular, are candidates for gossamer structures because they can be realized on thin membranes. However, the efficiency of reflectarray antennas decreases with any deviations from a flat plane, including those induced by plastically deformed creases. Rolling membranes can prevent some plastic deformation, but requires relative slipping between layers that are rolled together. In this work, we describe, analyze, and demonstrate two rolled configurations for deployable gossamer …

Secured Communication And Coexistence For Efficient Spectrum Utilization, Xue Wei

Electronic Theses & Dissertations (2024 - present)

Over the years, the demand for wireless communications has grown exponentially, resulting in a scarcity of available spectrum. This scarcity leads to the need for efficient spectrum utilization, a key factor in ensuring reliable and uninterrupted communications. One of the key challenges in achieving reliable communication is ensuring secure transmissions to protect the privacy and integrity of data for active users. Wireless communications are vulnerable to eavesdropping, interception, and unauthorized access, which can compromise the confidentiality and integrity of transmitted data. The coexistence of multiple wireless communication systems is another challenge to be addressed for passiver users. In many cases, …

Semi-Quantitative Elemental Imaging Of Corrosion Products From Bioabsorbable Mg Vascular Implants In Vivo, Weilue He, Keith W. Macrenaris, Adam Griebel, Maria P. Kwesiga, Erico Freitas, Amani Gillette, Jeremy Schaffer, Thomas V. O'Halloran, Roger J. Guillory Ii

Michigan Tech Publications

While metal materials historically have served as permanent implants and were designed to avoid degradation, next generation bioabsorbable metals for medical devices such as vascular stents are under development, which would elute metal ions and corrosion byproducts into tissues. The fate of these eluted products and their local distribution in vascular tissue largely under studied. In this study, we employ a high spatial resolution spectrometric imaging modality, laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS) to map the metal distribution, (herein refered to as laser ablation mapping, or LAM) from Mg alloys within the mouse vascular system and approximate …

Evaluation Of Practical Methods To Determine If A Karst Creek Is Gaining Or Losing: Case Study Of Leith Creek, Elizabeth Jones

Graduate Theses/Dissertations

Karst landscapes are abundant in Missouri, with features such as caves, springs, and sinkholes that form through the dissolution of limestone. Leith Creek is a small stream in Polk County, Missouri fed by two springs and the shallow unconfined Springfield Plateau aquifer, a highly karstified aquifer which is made up of limestone and minor interbedded shale-mudstone units. To determine if Leith Creek is gaining or losing, stream flow, water chemistry and temperature sensors were monitored. Stream flow results required multiple visits to take measurements while temperature sensors required two visits, one to install the dataloggers and another to remove the …

A Generalized Coherence Framework For Quantifying Input Contributions In Multi-Input Systems With Correlated Or Uncorrelated Inputs, Nolan H. Howes, Matthew S. Allen, Dario Farina, Steven Knight Charles

Faculty Publications

In multi-input systems, it is often necessary to quantify the contribution of each input to an output. Such contribution analysis is frequently performed using coherence. However, when correlation is present between inputs, existing coherence measures do not accurately quantify the contribution of individual inputs, except in special cases. Here we propose an expanded coherence framework that enables contribution analysis in any multi-input system, regardless of input correlation. We bridged the gap by defining three new coherence measures: component, excluded, and isolated coherence. Component coherence is an intermediate measure that decomposes measured output power into components attributable to inputs directly vs …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Uncertainty-Aware Deep Learning Framework For Forecasting Coastal Water Level In Virginia Beach, Md Mahmudul Hasan, Malachi Schram, Sridhar Katragadda, Diana Mcspadden, Alisa N. Udomvisawakul, Heather Richter, Frank Liu

Computer Science Faculty Publications

Coastal areas like Virginia Beach, USA, are increasingly vulnerable to flooding. To mitigate the impact of flooding, it is crucial for the City of Virginia Beach to have reliable 72-hour-ahead (3 days) forecasts of water levels at key gauge locations. To support this effort, several sensors have been installed throughout the city to monitor water levels and other environmental parameters such as wind speed, precipitation, and atmospheric pressure. Leveraging sensor data from one of these locations, we developed an uncertainty-aware deep learning model to forecast water levels. We employed deep quantile regression (DQR) to quantify variability in the predictions and …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Normalizing Images In Various Weather And Lighting Conditions Using Colorpix2pix Generative Adversarial Network, Sanjida Tasnim, Ashif Mahmud Mostafa, Azmain Morshed, Namreen Shaiyaz, Shakib Mahmud Dipto, Saad Aloteibi, Mohammad Ali Moni, Md. Golam Rabiul Alam, Md. Ashraful Alam

Computer Science Faculty Publications

Autonomous vehicles (AVs) are widely regarded as the future of transportation due to their tremendous benefits and user comfort. However, the AVs have been struggling with very crucial challenges, such as achieving reliable accuracy in object detection as well as faster computation required for quick decision-making. In recent years, perception systems in driverless cars have been significantly enhanced, mainly due to advances in deep-learning-based object detection systems. However, these perception systems are still heavily affected by environmental variables, such as changes in illumination, refractive interference, and adverse weather conditions, which may compromise their reliability and safety. This research proposes an …

Insights In Adaptation: Examining Self-Reflection Strategies Of Job Seekers With Visual Impairments In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Significant changes in the digital employment landscape, driven by rapid technological advancements and the COVID-19 pandemic, have introduced new opportunities for blind and visually impaired (BVI) individuals in developing countries like India. However, a significant portion of the BVI population in India remains unemployed despite extensive accessibility advancements and job search interventions. Therefore, we conducted semi-structured interviews with 20 BVI persons who were either pursuing or recently sought employment in the digital industry. Our findings reveal that despite gaining digital literacy and extensive training, BVI individuals struggle to meet industry requirements for fulfilling job openings. While they engage in self-reflection …

S²Il: Structurally Stable Incremental Learning, S. Balasubramanian, P. Yedu Krishna, Talasu Sai Sriram, M. Sai Subramaniam, Manepalli Pranav Phanindra Sai, Ravi Mukkamala

Computer Science Faculty Publications

Feature Distillation (FD) strategies are proven to be effective in mitigating Catastrophic Forgetting (CF) seen in Class Incremental Learning (CIL). However, current FD approaches enforce strict alignment of feature magnitudes and directions across incremental steps, limiting the model’s ability to adapt to new knowledge. In this paper, we propose Structurally Stable Incremental Learning (S²IL), a FD method for CIL that mitigates forgetting by focusing on preserving the overall spatial patterns of features which promote flexible (plasticity) yet stable representations that preserve old knowledge (stability). We also demonstrate that our proposed method S²IL achieves strong incremental accuracy and outperforms other FD …

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Oneapi Esimd, Joseph Wassell, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Timothée Ewart

Computer Science Faculty Publications

This paper presents an efficient implementation of a linear-solver kernel relevant to FUN3D, a suite of computational fluid dynamics software developed at NASA’s Langley Research Center. The linear solver is optimized for a range of block sizes commonly used in FUN3D. The implementation targets Aurora, the Argonne Leadership Computing Facility’s (ALCF) exascale machine featuring Intel Data Center Max 1550 GPUs. The linear solver’s performance is memory bandwidth-bound due to its low arithmetic intensity. The primary performance challenges stem from variable matrix row lengths and indirect memory access patterns inherent in unstructured-grid applications. Variable block sizes introduce additional complexity through differing …

Delivery Of Medical Supplies To Remote Locations Via Unmanned Aerial Vehicles: Approaches, Challenges, And Solutions, Meshari Aljohani, Ravi Mukkamala, Stephanie Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming more important in improving healthcare logistics, in particular due to their cost effectiveness, minimized risk, and versatile operational capabilities. This study explores the deployment of autonomous UAVs to deliver medical supplies to remote areas. Advances in ledger technology, smart contracts, and machine learning have transformed tasks previously managed by human teams or manually controlled UAVs into fully autonomous missions. We present a comprehensive analysis of the challenges and initial solutions vital for the effective use of autonomous UAVs in the delivery of medical supplies. In addition, we propose a machine-learning model to optimize UAV …

Investigation Of New Superconducting Materials For The Next Generation High-Performance Rf Superconducting Cavities For Particle Accelerators, Alex Gurevich, Jean Delayen, Chang-Beom Eom, Gianluigi Ciovati

Physics Faculty Publications

In this DOE-funded project DE-SC0010081-020 Old Dominion University (ODU) in collaboration with University of Wisconsin (UW) and Jefferson Laboratory have investigated both experimentally and theoretically electromagnetic response and losses in multilayered superconducting structures made of new SRF materials which can push the field and Q performance limits of accelerating cavities.

Modeling Strain And Quantum Confinement In Gaas/GaXIn1-XP Superlattices For Spin-Polarized Electron Sources, A. Kachwala, G. Blume, S. Marsillac, J. Grames, M. Grau

Physics Faculty Publications

In this study, we systematically design and simulate a series of GaAs-based superlattice configurations aimed at enhancing heavy-hole–light-hole band splitting while simultaneously optimizing band alignment to reduce the conduction band barrier, thereby facilitating efficient electron transport. These combined effects are crucial for achieving high electron spin polarization and high quantum efficiency, the two key performance metrics of next-generation spin-polarized electron sources. We investigated three types of superlattice architectures: (1) compressively strained GaAs wells on GaInP barriers, yielding a maximum band splitting of 140 meV, (2) lattice-matched GaAs/GaInP structures, resulting in the maximum band splitting of 75 meV, and (3) tensile …

A Biphasic Microstructural Model Of Skeletal Muscle To Study Structure-Function Mechanisms Under Multiaxial Tensile And Compressive Conditions, Benjamin Wheatley, Minhaj U. Bhuiyan, Sabrina S. Lorza, Kevin M. Moerman, Pierre-Yves Rohan

Faculty Journal Articles

No abstract provided.

A Framework For Fair And Trustworthy Multimodal Video Anomaly Detection: Fusion, Dataset Quality Auditing, Explainability, And Fairness, Omeshamisu Judith Anigala

Electronic Theses and Dissertations

No abstract provided.

Largely Enhanced Out-Of-Plane Electromechanical Coupling Effects In Two-Dimensional Molybdenum-Disulfide/Boron-Nitride Heterostructures, Qiong Liu, Vijay Kumar Choyal, James E. Morris, Timon Rabczuk, Xiaoning Jiang, Xiaoying Zhuang

Electrical and Computer Engineering Faculty Publications and Presentations

The electromechanical coupling effects in two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted great interest. However, for 2D TMDs, piezoelectricity is confined to the basal plane, and the flexoelectricity-derived out-of-plane electromechanical response is usually faint, limiting the applications of this material family using the out-of-plane electromechanical effects. Here, this work reports a facile strategy to greatly enhance the out-of-plane electromechanical response of hexagonal molybdenum disulfide (2H-MoS₂) nanoflakes by stacking monolayer hexagonal boron nitride (h-BN) on 2H-MoS₂ nanoflakes to form MoS₂/BN heterostructures. The d^eff/₃₃ coefficient of MoS₂/BN can reach a value comparable …

Ecobuoys For Scalable Oceanography, Anuscheh Nawaz, Michael Steele, Ruth Branch, David Burnett, Kuotian Liao, Mallory Parker, Eleftheria Roumeli

Electrical and Computer Engineering Faculty Publications and Presentations

An approach to scalable surface-drifting buoys is needed to enable the high spatial and temporal resolution of oceanographic data that the science and meteorological communities are asking for. With the number of active buoys predicted to increase by a factor of 100 or more, the impact on the environment becomes even more important. Here, we present a pathway to a scalable and sustainable generation of buoys. We identify the main criteria to be used when developing such buoys to be low cost, with reliable data and neutral or even positive environmental impact. For each buoy subsystem—hull, electronics, energy generation and …

An Offline Approach For Frequency Event Detection In Power Systems: Tkeo And Statistical Analysis, Hussain A. Alghamdi, Midrar Adham, Umar Farooq, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

The increasing integration of renewable energy sources in power grids and the transition from conventional thermal-based generation to inverter-based resources for power generation have reduced power system inertia and increased the rate of change of frequency, which constitutes a challenge for frequency stability in modern power systems. These changes necessitate robust frequency event detection algorithms that can rapidly and accurately identify events, providing essential support to maintain stability. This paper proposes a frequency event detection algorithm with six tunable parameters for offline post-processing, leveraging the Teager-Kaiser Energy Operator method and statistical analysis. The algorithm is tested on a dataset comprised …

Work In Progress: Using Internships As Means For Indirect Assessment Of Abet Criteria 3 “1-7” Student Outcomes, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

The Electrical & Computer Engineering (ECE) Department at Portland State University (PSU) has developed a Power Engineering Internship (PEI) program that provides engineering career development pathways within the electric utility industry. The PEI is supported by several U.S. Department of Energy grants that aim to develop quality career opportunities and develop a future electric utility workforce that represents the nation’s diverse populations. The PSU ECE Department intends to use surveys of internship participants as assessment tools for its ABET accreditation process, in particular, the ABET Criteria 3 Student Outcomes (SOs). SOs relate to the knowledge, skills, and behaviors that students …

Environmentally Friendly Chelation For Enhanced Algal Biomass Deashing, Agyare Asante, George Daramola, Ryan W. Davis, Sandeep Kumar

Civil & Environmental Engineering Faculty Publications

High ash content in algal biomass limits its suitability for biofuel production by reducing combustion efficiency and increasing fouling. This study presents a green deashing strategy using nitrilotriacetic acid (NTA) and deionized (DI) water to purify Scenedesmus algae, which was selected for its high ash removal potential. The optimized sequential treatment (DI, NTA chelation, and DI+NTA treatment at 90–130 °C) achieved up to 83.07% ash removal, reducing ash content from 15.2% to 3.8%. Elevated temperatures enhanced the removal of calcium, magnesium, and potassium, while heavy metals like lead and copper were reduced below detection limits. CHN analysis confirmed minimal …

Sustainable Management Of Erosive Shores: An Interdisciplinary Approach Integrating Engineering And Social Sciences At A Tide-Dominant Beach Area, Jun Ik Sohn, Hyun Dong Kim, Kiran Adhithya Ramakrishnan

Civil & Environmental Engineering Faculty Publications

This study investigates the causes and consequences of shoreline erosion at Kkotji Beach, a prominent tourist destination on the west coast of South Korea, where the degradation of the coastal environment has increasingly threatened the local tourism industry and economy, by employing a mixed-methods approach that combines field observations with MIKE 21 hydrodynamic simulations and by integrating perspectives from coastal engineering and the social sciences to develop practical, site-specific strategies for mitigating erosion, enhancing public awareness, and promoting sustainable coastal planning and development that support long-term environmental resilience and economic stability. The results show that dominant ebb currents drive southward …

Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park

Engineering Management & Systems Engineering Faculty Publications

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model …

Proactive Distributed Emergency Response With Heterogeneous Tasks Allocation, Justice Darko, Hyoshin Park

Engineering Management & Systems Engineering Faculty Publications

Traditionally, traffic incident management (TIM) programs coordinate the deployment of emergency resources to immediate incident requests without accommodating the interdependencies on incident evolutions in the environment. However, ignoring these inherent interdependencies while making current deployment decisions is shortsighted, and the resulting naive deployment strategy can significantly worsen the overall incident delay impact on the network. The interdependencies on incident evolution in the environment, including those between incident occurrences and those between resource availability in near-future requests and the anticipated duration of the immediate incident request, should be considered through a look-ahead model when making current-stage deployment decisions. This study develops …

Quantifying Multidimensional Effects Of Physicochemical Parameters On Pfas Adsorption Using A Hybrid Response Surface Methodology-Machine Learning Approach, Harsh V. Patel, Jazmin Green, John Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) contamination has posed a significant environmental and public health challenge due to their ubiquitous nature. Adsorption has emerged as a promising remediation technique, yet optimizing adsorption efficiency remains complex due to the diverse physicochemical properties of PFAS and the wide range of adsorbent materials. Traditional modeling approaches, such as response surface methodology (RSM), struggled to capture nonlinear interactions, while standalone machine learning (ML) models required extensive datasets. This study addressed these limitations by developing hybrid RSM-ML models to improve the prediction and optimization of PFAS adsorption. A comprehensive dataset was constructed using experimental adsorption data, …